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/*! \file
    \brief Templates implementing warp-level matrix multiply-accumulate
   operations targeting Tensor Cores.

    This is a work in progress.
*/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/array.h"

#include "cutlass/numeric_types.h"
#include "cutlass/matrix_shape.h"

#include "cutlass/arch/mma.h"

#include "cutlass/gemm/gemm.h"
#include "cutlass/gemm/warp/mma.h"

#include "cutlass/gemm/warp/mma_tensor_op_policy.h"
#include "cutlass/gemm/warp/mma_tensor_op_tile_iterator_sm70.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace gemm {
namespace warp {

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Structure to compute the matrix product targeting CUDA cores and SIMT math
/// instructions.
template <
        /// Size of the Gemm problem - concept: gemm::GemmShape<>
        typename Shape_,
        /// Data type of A elements
        typename ElementA_,
        /// Layout of A matrix (concept: MatrixLayout)
        typename LayoutA_,
        /// Data type of B elements
        typename ElementB_,
        /// Layout of B matrix (concept: MatrixLayout)
        typename LayoutB_,
        /// Element type of C matrix
        typename ElementC_,
        /// Layout of C matrix (concept: MatrixLayout)
        typename LayoutC_,
        /// Policy describing warp-level MmaTensorOp (concept: MmaTensorOp
        /// policy)
        typename Policy_,
        /// Used for partial specialization
        typename Enable = bool>
class MmaVoltaTensorOp {
public:
    /// Shape of warp-level matrix operation (concept: GemmShape)
    using Shape = Shape_;

    /// Data type of multiplicand A
    using ElementA = ElementA_;

    /// Layout of multiplicand A
    using LayoutA = LayoutA_;

    /// Data type of multiplicand B
    using ElementB = ElementB_;

    /// Layout of multiplicand B
    using LayoutB = LayoutB_;

    /// Data type of accumulator matrix C
    using ElementC = ElementC_;

    /// Layout of accumulator matrix C
    using LayoutC = LayoutC_;

    /// Shape of the warp in units of thread (concept: MmaLanePolicySimt)
    using Policy = Policy_;

    /// Indicates class of matrix operator
    using OperatorClass = arch::OpClassTensorOp;

    /// Architecture tag
    using ArchTag = arch::Sm70;

    /// Underlying matrix multiply operator (concept: arch::Mma)
    using ArchMmaOperator = typename Policy::Operator;

    /// Underlying instruction shape
    using InstructionShape = typename ArchMmaOperator::Shape;

    /// Complex transform on A operand
    static ComplexTransform const kTransformA = ComplexTransform::kNone;

    /// Complex transform on B operand
    static ComplexTransform const kTransformB = ComplexTransform::kNone;

    /// Number of threads participating in warp-level matrix product
    static int const kThreadCount = 32;

    /// interleaved 32x32 tiles
    using InterleavedTileShape = GemmShape<32, 32, 4>;

    static_assert(!(Shape::kM % InterleavedTileShape::kM) &&
                          !(Shape::kN % InterleavedTileShape::kN),
                  "Shape must be a multiple of InterleavedTileShape.");

public:
    /// Iterates over the A operand in memory
    using IteratorA = MmaVoltaTensorOpMultiplicandTileIterator<
            MatrixShape<Shape::kM, Shape::kK>, Operand::kA, ElementA, LayoutA,
            MatrixShape<ArchMmaOperator::Shape::kM, ArchMmaOperator::Shape::kK>,
            Policy::OpDelta::kRow, kThreadCount>;

    /// Storage for A tile
    using FragmentA = typename IteratorA::Fragment;

    /// Iterates over the B operand in memory
    using IteratorB = MmaVoltaTensorOpMultiplicandTileIterator<
            MatrixShape<Shape::kK, Shape::kN>, Operand::kB, ElementB, LayoutB,
            MatrixShape<ArchMmaOperator::Shape::kK, ArchMmaOperator::Shape::kN>,
            Policy::OpDelta::kRow, kThreadCount>;

    /// Storage for B tile
    using FragmentB = typename IteratorB::Fragment;

    /// Iterates over the C operand in memory
    using IteratorC = MmaVoltaTensorOpAccumulatorTileIterator<
            MatrixShape<Shape::kM, Shape::kN>, ElementC, LayoutC,
            typename ArchMmaOperator::Shape, typename Policy::OpDelta>;

    /// Storage for C tile
    using FragmentC = typename IteratorC::Fragment;

private:
    static_assert(
            !(Shape::kM % ArchMmaOperator::Shape::kM) &&
                    !(Shape::kN % ArchMmaOperator::Shape::kN),
            "Shape of warp-level Mma must be divisible by operator shape.");

    /// Number of mma operations performed
    using MmaIterations =
            MatrixShape<InterleavedTileShape::kM / ArchMmaOperator::Shape::kM,
                        InterleavedTileShape::kN / ArchMmaOperator::Shape::kN>;
    using TileIterations = MatrixShape<Shape::kM / InterleavedTileShape::kM,
                                       Shape::kN / InterleavedTileShape::kN>;

    // Whether matrix B is reordered
    bool reorder_B_;

public:
    /// Underlying matrix multiply operator (concept: arch::Mma)
    ArchMmaOperator mma;

public:
    //
    // Methods
    //

    /// Ctor
    CUTLASS_DEVICE
    MmaVoltaTensorOp() {}

    /// Performs a warp-level matrix multiply-accumulate operation
    CUTLASS_DEVICE
    void operator()(FragmentC& D, FragmentA const& A, FragmentB const& B,
                    FragmentC const& C) {
        using MmaOperandA = typename ArchMmaOperator::FragmentA;
        using MmaOperandB = typename ArchMmaOperator::FragmentB;
        using MmaOperandC = typename ArchMmaOperator::FragmentC;

        D = C;

        MmaOperandA const* ptr_A = reinterpret_cast<MmaOperandA const*>(&A);
        MmaOperandB const* ptr_B = reinterpret_cast<MmaOperandB const*>(&B);
        MmaOperandC* ptr_D = reinterpret_cast<MmaOperandC*>(&D);

        CUTLASS_PRAGMA_UNROLL
        for (int outer_col = 0; outer_col < TileIterations::kColumn;
             ++outer_col) {
            CUTLASS_PRAGMA_UNROLL
            for (int inner_col = 0; inner_col < MmaIterations::kColumn;
                 ++inner_col) {
                CUTLASS_PRAGMA_UNROLL
                for (int outer_row = 0; outer_row < TileIterations::kRow;
                     ++outer_row) {
                    CUTLASS_PRAGMA_UNROLL

                    for (int inner_row = 0; inner_row < MmaIterations::kRow;
                         ++inner_row) {
                        int op_col =
                                inner_col + MmaIterations::kColumn * outer_col;

                        // Column-major serpentine sequence to maximize reuse of
                        // A operand.
                        int inner_row_serp = inner_row;
                        int outer_row_serp = outer_row;
                        if (op_col & 1) {
                            inner_row_serp =
                                    MmaIterations::kRow - inner_row - 1;
                            outer_row_serp =
                                    TileIterations::kRow - outer_row - 1;
                        }
                        int op_row = inner_row_serp +
                                     MmaIterations::kRow * outer_row_serp;
                        int op_idx = inner_row_serp +
                                     MmaIterations::kRow *
                                             (inner_col +
                                              MmaIterations::kColumn *
                                                      (outer_row_serp +
                                                       TileIterations::kRow *
                                                               outer_col));
                        mma(ptr_D[op_idx], ptr_A[op_row], ptr_B[op_col],
                            ptr_D[op_idx]);
                    }
                }
            }
        }
    }
};

/////////////////////////////////////////////////////////////////////////////////////////////////

}  // namespace warp
}  // namespace gemm
}  // namespace cutlass
